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Crimes are a social irritation and cost our society deeply in several ways. Any research that can help in solving crimes quickly will pay for itself. About 10% of the criminals commit about 50% of the crimes [9]. The system is trained by feeding previous years record of crimes taken from legitimate online portal of India listing various crimes such as murder, kidnapping and abduction, dacoits, robbery,...
Association rule mining is a standard technique used for finding the relationships among the itemsets in a database. The method of extracting the frequent itemsets from the database using existing algorithms has several disadvantages such as generation of large number of candidate itemsets, increase in computational time and database scan. With this aim, the paper proposes Mining Interesting Itemsets...
Finding the most interesting correlations among items is essential for problems in many commercial, medical, and scientific domains. Our previous work on the maximal fully-correlated itemset (MFCI) framework can rule out the itemsets with irrelevant items and its downward-closed property helps to achieve good computational performance. However, to calculate the desired MFCIs in large databases, there...
Due to prevalent use of sensors and network monitoring tools, big volumes of data or “big data” today traverse the enterprise data processing pipelines in a streaming fashion. While some companies prefer to deploy their data processing infrastructures and services as private clouds, others completely outsource these services to public clouds. In either case, attempting to store the data first for...
Spatial autocorrelation is a very general statistical property of economic variables, it indicates correlation of a variable with itself through space. Spatial association rule mining, discovery of interesting, meaningful rules in spatial databases, ignores autocorrelation of the spatial data, or just generalizes the spatial data into attribute data currently. In order to compare the results between...
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